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Income inequality in European Regions: Recent trends and determinants

Ungleichheit in Europäischen Regionen: Letzte Trends und Determinanten

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Abstract

Income inequality is both at the political and academic agenda. Because of the Great Recession, income inequality has experienced an increase in many parts of the world in general and in many European regions in particular. In addition, several academics have signalled inequality as a source of such crisis. Nevertheless, few attempts have been made for conducting the analysis at the regional level. In this work we analyse the main factors behind current trends in inequality in Europe over the last decade. We develop our analysis at the regional level, which adds a new dimension to the existing literature. Our results point to a large diversity in inequality patterns. Inequality is on average lower in more developed regions, but recent increases in inequality seem associated with economic growth. Our results suggest that tertiary specialisation, openness, and technological change, although likely to be associated with economic growth, are also associated with increasing inequalities.

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Notes

  1. See Ehrhart (2009), Galor (2009), Neves and Silva (2014) and Castells-Quintana and Royuela (2014a) for comprehensive literature reviews on the transmission channels between inequality and economic growth.

  2. See Perachi (2002) and Tselios (2014) for further details on ECHP.

  3. This work considers the seven available waves, from 2004 to 2012. The 2004 data wave is only available for few countries and the 2012 wave does not provide information for Belgium and Ireland. Data for 2005 and 2006 is also incomplete for some countries. Table 6 in Appendix 1 lists all regions considered (in the ECHP and in the EU-SILC samples).

  4. Till and Eiffe (2010) also highlight a number of variables that had been covered in the earlier ECHP but which are not available in EU-SILC.

  5. As argued by Iacovou et al (2012) and Atkinson and Marlier (2010), data on income are, however, subjected to other potential problems that affect similar household surveys: recall, timeliness and temporal mismatch between the “income reference period”—the time frame to which the income data refers –, and the “current reference period”—the time frame to which almost all the other data in the survey refers, namely the moment of interview.

  6. Household members are made equivalent using an equivalence scale, which gives a weight to all members of the household: 1.0 to the first adult; 0.5 to the second and each subsequent person aged 14 and over; and 0.3 to each child under the age of 14. Then these are added up to arrive at the equivalised household size.

  7. Income inequality measures for all the considered regions and periods can be found in the Electronic Supplementary Material to this article.

  8. Harrison and Bluestone (1988) refer to “the Great U-Turn” in relation to the rising inequalities of the late 20th in the U.S. after several decades of declining inequality. Evidence of this “turn” has been found not only for the U.S. but also for other post-industrialised countries. Others, as Conceiçao and Galbraith (2001), refer to an “augmented-Kuznets Curve” to describe the same phenomenon of rising inequalities after the inverted-U experience. No paper, to the best of our knowledge, has identified nor explained yet, in a regional analysis for several countries, this U-shaped part of the N-shaped relationship between development and inequality.

  9. Following Baltagi and Griffin (1984) and Pirotte (1999), the cross section and between estimates would represent the long-run impact of the explanatory variable, while the fixed effects estimates would capture the short-run impact of the variable, with the random effects parameter a mix of the between and fixed effects estimate.

  10. These differences have been tested by means of the difference in parameters and the square root of the main diagonal of the joint variance matrix that uses the Hausman test.

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Acknowledgements

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 216813 and n° 266834. We make use of microdata from the European Commission, Eurostat, cross-sectional ECHP and EU-SILC databases made available by Eurostat under contracts ECHP/2009/06 and EU-SILC/2012/17. Eurostat has no responsibility for the results and conclusions reported here. We are grateful for Maria Iacovou for providing us data on family structure that we use in this work. We also acknowledge the financial support of CICYT ECO2013-41022-R.

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Correspondence to Vicente Royuela.

Appendix

Table 6 Regions considered (NUTS 1 regions in ECHP and SILC)
Table 7 Variables definition and sources
Table 8 Descriptive statistics
Table 9 Correlation matrix

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Castells-Quintana, D., Ramos, R. & Royuela, V. Income inequality in European Regions: Recent trends and determinants. Rev Reg Res 35, 123–146 (2015). https://doi.org/10.1007/s10037-015-0098-4

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